GVICA: A Multi-Channel EEG Hierarchical Noise Reduction Framework Based on GWO Dynamically Optimized VMD-ICA Fusion

This study provides a reliable tool for cleaning electroencephalogram (EEG) data in clinical settings and demonstrates significant potential for application in the analysis of motor execution EEG during stroke rehabilitation.

Authors

Yaping Huai (ORCID: https://orcid.org/0000-0002-5379-2940)
C. F. Xu (ORCID: https://orcid.org/0000-0002-1290-7860)
Haiqing He
Yiqing Lu
Qing Liu
Yongcheng Li

Institutions

Guangdong University of Technology (CN)
Chinese Academy of Sciences (CN)
Longgang Central Hospital (CN)
Shenzhen Institutes of Advanced Technology (CN)

Publication Details

Journal: IEEE Transactions on Biomedical Engineering
Published: 2026-01-01
DOI: https://doi.org/10.1109/tbme.2026.3677897
Primary Topic: EEG and Brain-Computer Interfaces
Type: article
Field-Weighted Citation Impact: 0.00

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article

GVICA: A Multi-Channel EEG Hierarchical Noise Reduction Framework Based on GWO Dynamically Optimized VMD-ICA Fusion

Yaping Huai, C. F. Xu, Haiqing He, Yiqing Lu et al.

Jan 1, 2026

IEEE Transactions on Biomedical Engineering

EEG and Brain-Computer Interfaces

article

GVICA: A Multi-Channel EEG Hierarchical Noise Reduction Framework Based on GWO Dynamically Optimized VMD-ICA Fusion

Yaping Huai, C. F. Xu, Haiqing He, Yiqing Lu, Qing Liu, Yongcheng Li

January 1, 2026article en

Abstract

This study provides a reliable tool for cleaning electroencephalogram (EEG) data in clinical settings and demonstrates significant potential for application in the analysis of motor execution EEG during stroke rehabilitation.

IEEE Transactions on Biomedical EngineeringVol. PP

Guangdong University of Technology (CN), Chinese Academy of Sciences (CN), Longgang Central Hospital (CN), Shenzhen Institutes of Advanced Technology (CN)

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Openalex Percentile: Top 10%

EEG and Brain-Computer Interfaces

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